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Review product metrics, spot trends, and get actionable improvement recommendations.
The material indicates a prompt-only open-source skill with no required secrets, no declared remote endpoints, and no evident local code execution or file/system access. Overall risk is low, with minor caution only around limited community adoption and unclear maintenance details.
The material explicitly states that no keys or environment variables are required; as a prompt-only skill, there is no evident design for collecting, storing, or transmitting credentials, so credential exposure and abuse risk is low.
No remote endpoints are declared, and the system flags it as prompt-only. The README only describes pulling data if an analytics tool is already connected; this skill itself does not show independent network access or outbound transfer of user data.
The material does not indicate launching local processes, executing scripts, or invoking system commands; as a skill document/prompt workflow, it shows no execution capability beyond text analysis and guidance.
No file system, database, or local resource read/write permissions are declared; data appears to come from user-provided content or already connected analytics tools, and the skill itself does not show excessive access.
The source is an open-source GitHub repository, which improves auditability and lowers overall risk; however, the license is unspecified, community adoption is 0 stars, and maintenance status is unknown, so supply-chain confidence and maintenance assurance are limited. Review repository contents and update history before use.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "metrics-review" skill from askskill: 1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/product-management/skills/metrics-review/SKILL.md 2. Save it as ~/.claude/skills/metrics-review/SKILL.md 3. Reload skills and tell me it's ready
Please review the following weekly product metrics: new users 12,400, up 8% WoW; DAU 85,000, down 3% WoW; day-2 retention 41%, down 5 percentage points; paid conversion 2.8%, up 0.4 percentage points. Output: 1) key trend summary, 2) abnormal metrics and possible causes, 3) priority actions for next week, and 4) concise takeaways for reporting.
A structured weekly review summarizing trends, highlighting issues like retention decline, and recommending next-step actions.
Our signup conversion rate dropped from 18% to 11% over the past 3 days. Act as a data analysis advisor: list possible causes and prioritize them, provide an investigation framework covering traffic sources, page changes, tracking issues, channel quality, and system failures, then give emergency recommendations and a checklist of additional data needed.
A troubleshooting plan with hypotheses, priorities, analysis steps, and short-term mitigation recommendations.
Turn the following monthly metrics into an executive scorecard: revenue target 5.0M, actual 4.6M; active users target 1.2M, actual 1.28M; churn target 4%, actual 5.6%; NPS target 45, actual 43. Output by achievement status, business impact, risk alerts, and recommended actions, and label each metric with red/yellow/green status.
A clear monthly scorecard showing target attainment, risk levels, and management recommendations.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Review and analyze product metrics, identify trends, and surface actionable insights.
/metrics-review $ARGUMENTS
If ~~product analytics is connected:
If no analytics tool is connected, ask the user to provide:
Ask the user:
Structure the review using a metrics hierarchy: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down. See Product Metrics Hierarchy below for full definitions.
If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.
For each key metric:
Identify correlations:
2-3 sentences: overall product health, most notable changes, key callout.
Table format for quick scanning:
| Metric | Current | Previous | Change | Target | Status |
|---|---|---|---|---|---|
| [Metric] | [Value] | [Value] | [+/- %] | [Target] | [On track / At risk / Miss] |
For each metric worth discussing:
What is going well:
What needs attention:
Specific next steps based on the analysis:
After generating the review:
The single metric that best captures the core value your product delivers to users. It should be:
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